https://github.com/RudreshVeerkhare/StyleGan
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
3D Face Reconstruction
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3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/RuisongZhou/RetinaFace.pytorch
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/Ruoyiran/DeepID
Deep Learning Face Representation by Joint Identification-Verification
https://github.com/RuoyuChen10/objectdetection-saliency-maps
https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Ruturaj123/VGG16-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/RyanWu2233/Style_GAN2_FFHQ
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/SANKHA1/Vehicle-Detection
https://github.com/SBPL-Cruz/perch_pose_sampler
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/SHI-Labs/Neighborhood-Attention-Transformer
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SSusantAchary/FaceDetection-counting-using-MTCNN
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/SY-Xuan/pointnet_paddle
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/Saiyam26/Deep-Pixel-wise-Binary-Supervision
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/Saiyam26/Face-Anti-Spoofing-using-DeePixBiS
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/Sakib1263/1DResNet-Builder-KERAS
https://github.com/Sakib1263/1DResNet-KERAS
https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-Model-Builder-KERAS
https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS
https://github.com/Sakib1263/VGG-1D-2D-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-KERAS
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SamarpanDas/Face-Recognition-Face-Verification
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Sangkwun/Resnet
https://github.com/Sangohe/pgan_thesis
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/SanketAgrawal/Catagorical-Focal-Loss
https://github.com/SaoYan/GenerativeSkinLesion
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/SarthakGarg13/OTOMYCOSIS
https://github.com/Sarthaks21/Facenet-with-MTCNN-and-SVM
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Satyake/Triplet-Loss_Siamese_NN
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Saurav0074/Cartoon-Face-Detection-and-Recognition
Bringing Cartoons to Life: Towards Improved Cartoon Face Detection and Recognition Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.